Implementing PointNet for point cloud segmentation in the heritage context

Haznedar, Bulent, Bayraktar, Rabia, Ozturk, Ali Emre and Arayici, Yusuf (2023) Implementing PointNet for point cloud segmentation in the heritage context. Heritage Science, 11 (1). p. 2. ISSN 2050-7445

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Automated Heritage Building Information Modelling (HBIM) from the point cloud data has been researched in the last decade as HBIM can be the integrated data model to bring together diverse sources of complex cultural content relating to heritage buildings. However, HBIM modelling from the scan data of heritage buildings is mainly manual and image processing techniques are insufficient for the segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Artificial Intelligence (AI) based deep learning methods such as PointNet are introduced in the literature for point cloud segmentation. Yet, their use is mainly for manufactured and clear geometric shapes and components. To what extent PointNet based segmentation is applicable for heritage buildings and how PointNet can be used for point cloud segmentation with the best possible accuracy (ACC) are tested and analysed in this paper. In this study, classification and segmentation processes are performed on the 3D point cloud data of heritage buildings in Gaziantep, Turkey. Accordingly, it proposes a novel approach of activity workflow for point cloud segmentation with deep learning using PointNet for the heritage buildings. Twenty-eight case study heritage buildings are used, and AI training is performed using five feature labelling for segmentation namely, walls, roofs, floors, doors, and windows for each of these 28 heritage buildings. The dataset is divided into clusters with 80% training dataset and 20% prediction test dataset. PointNet algorithm was unable to provide sufficient accuracy in segmenting the point clouds due to deformation and deterioration on the existing conditions of the heritage case study buildings. However, if PointNet algorithm is trained with the restitution-based heritage data, which is called synthetic data in the research, PointNet algorithm provides high accuracy. Thus, the proposed approach can build the baseline for the accurate classification and segmentation of the heritage buildings.

Item Type: Article
Additional Information: Funding information: This paper is written within the TUBITAK 1001 project (Grant number: 119Y038) and the authors would like to acknowledge The Scientific and Technological Research Council of Turkey (TUBITAK) for their support.
Uncontrolled Keywords: Deep learning, Artifcial intelligence, Cultural heritage, Segmentation, 3D point cloud
Subjects: G700 Artificial Intelligence
H200 Civil Engineering
K200 Building
Department: Faculties > Engineering and Environment > Architecture and Built Environment
Depositing User: Elena Carlaw
Date Deposited: 05 Jan 2023 13:11
Last Modified: 05 Jan 2023 13:15

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